Gradient descent is an optimization algorithm widely utilized in the fields of machine learning and deep learning for identifying the optimal parameters of a model. It involves iteratively adjusting the parameter values in the negative gradient direction of the loss function, which measures the disparity between the predicted output of the model and the actual output.